Determination of targeted food recommendation

ABSTRACT

A computer-implemented method, computer program product, and system for generating a targeted menu item recommendation are provided. The targeted menu item recommendation includes receiving a menu item recommendation request, generating search criteria for the menu item recommendation request, retrieving menu information regarding the search criteria, assigning weighted values to the retrieved information based on the text of the menu item information, preferences of the user, and social media association values, and generating the targeted menu item recommendation.

BACKGROUND

The present invention relates generally to the fields of software-basedtext analytics and social media websites, and more particularly tosoftware-based text analytic processing of food reviews and menu items.

Software-based natural language processing type text analytics (SBTA) isknown. Under conventional STBA techniques, first, data corresponding toa piece of text (herein sometimes called the “subject text”) isprovided. This piece of text will generally be written in a “naturallanguage.” As used herein, the term “natural language” means as follows:any language which arises in an unpremeditated fashion as the result ofthe innate facility for language possessed by the human intellect.Examples of natural languages include regional and national spoken andwritten human languages such as English and Latin. The subject text, tobe subject to SBTA, is written, or otherwise generated, by an entitythat is herein sometimes called the “author.” The author is generally,but not necessarily, a human.

SBTA is the derivation of high-quality information from the subjectnatural language text using software based dictionaries and rules whichare applied to the subject natural language text. SBTA may include otherfeatures and/or characteristics, such as: (i) high-quality informationderived from natural language subject text through the devising ofpatterns and trends through means such as statistical pattern learning;(ii) text categorization; (iii) text clustering; (iv) concept/entityextraction; (v) production of granular taxonomies; (vi) sentimentanalysis; (vii) document summarization; (viii) entity relation modeling(that is, learning relations between named entities); (ix) informationretrieval; (x) lexical analysis to study word frequency distributions;(xi) pattern recognition; (xii) tagging/annotation; (xiii) informationextraction; (xiv) data mining techniques (including link and associationanalysis); (xv) visualization; (xvi) predictive analytics; (xvii) use ofparsing rules; and/or (xviii) use of character rules.

UIMA (Unstructured Information Management Architecture) is an industrystandard for content analytics, which can be used to help make SBTAsoftware for performing software-based natural language type textanalytics. UIMA is a component software architecture for thedevelopment, discovery, composition and/or deployment of multi-modalanalytics for the analysis of unstructured information and itsintegration with search technologies.

Social media websites (SMWs), and the software that creates, manages andcontrols SMWs, is also known. An SMW is an interactive web platform bywhich individuals and communities share, co-create, discuss, modifyuser-generated content, and/or mediate human communication.

In many SMWs, a user will have one or more groups of other selectedusers to whom the user is considered to be socially related. Thesegroups of socially-related users are typically called “friends,”“connections,” “circles,” or the like. The establishment of this socialrelationship (for example, friend, or connection) may require bothparties to agree to the designation of the social relationship, but thisis not always necessarily required. The establishment of the socialrelationship may require (or at least encourage) socially relatedparties to have a relationship outside of the context of the SMW, butthis is also not always necessarily required. Herein, socialrelationships like friends, connections, circles and the like will begenerically referred to as a “set of SMW-related-users” and/or“SMW-related-users.”

SUMMARY

Embodiments of the present invention disclose a computer-implementedmethod, computer program product, and system for generating a targetedmenu item recommendation. A menu item recommendation request is receivedfrom a user. Search criteria is determined from the menu itemrecommendation request. Menu item information and reviews satisfying thesearch criteria are retrieved. A value is given to each menu item andreview based on user preferences and SMW association values. Thetargeted menu item recommendation is generated based on the valuesassigned to the menu items and reviews and sent to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description, given by way of example and notintended to limit the invention solely thereto, will best be appreciatedin conjunction with the accompanying drawings, in which:

FIG. 1 is a functional block diagram illustrating a distributed dataprocessing environment, according to an exemplary embodiment;

FIG. 2 is a functional block diagram further illustrating the parts of aprogram, according to an exemplary embodiment;

FIG. 3 is a flowchart depicting operational steps of the program,according to an exemplary embodiment;

FIG. 4 is a block diagram of components of a server computer executingthe program, according to an exemplary embodiment;

FIG. 5 depicts a cloud computing environment, according to an exemplaryembodiment;

FIG. 6 depicts abstract model layers of a cloud computing environment,according to an exemplary embodiment.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention. In the drawings, like numbering representslike elements.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

References in the specification to “one embodiment”, “an embodiment”,“an exemplary embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

In many societies, people are highly mobile and have extensive access toinformation regarding particular destinations. Whether a person istraveling to a new region of the world or a different neighborhoodwithin walking distance of their home, information regarding theirdestination is readily available. Many applications are available toconsumers regarding restaurant menu items and reviews. However,oftentimes these applications provide too many suggestions, many ofwhich may not be geared towards a specific user's preferences.Furthermore, reviews are typically made by people not familiar to theuser, leaving the user with little information regarding the similarityin preferences between the user and the reviewer.

The present invention provides a system, a method, and/or a computerprogram product providing a user with targeted menu item recommendationsbased on user preferences, public reviews, and weighted friend reviews.One way to provide a user with targeted menu item recommendations usestext analytics, user preferences, and social media networks. Oneembodiment by which to provide a user with targeted menu itemrecommendations is described below referring to the accompanyingdrawings FIGS. 1-6. For the purposes of the described embodiment, theuser will request recommendations for a specific menu item, however, itshould be understood that the user request can pertain to other foodbased categories (e.g., restaurants and dish types).

FIG. 1 is a functional block diagram of targeted menu itemrecommendation system 100, according to an embodiment. The targeted menuitem recommendation system 100 includes server computer 102, SMW server106 and computing device 104 connected via network 110.

Network 110 can be, for example, a local area network (LAN), a wide areanetwork (WAN) such as the Internet, or a combination of the two, and caninclude wired, wireless, or fiber optic connections. In general, network110 can be any combination of connections and protocols that willsupport communications between server 102, SMW server 106 and computingdevice 104, in accordance with one or more embodiments of the invention.

Computing device 104 may be laptop computers, tablet computers, netbookcomputers, personal computers (PC), desktop computers, personal digitalassistants (PDA), smartphones, SMS capable phones, or any programmableelectronic device capable of communicating with server computer 102 andSMW Server 106 via network 110, in accordance with one or moreembodiments of the invention.

In an embodiment, computing device 104 is a device used forcommunication with a SMW (e.g., SMW server 106), such as, for example,Twitter® or Facebook®. Users of the SMW may have an SMW associationwhere each user may have mutually requested to have “associate” or“friend” status, as defined by the software of the SMW. This status isan example of being “SMW related” as that term is used in this document.For present purposes, the point is not so much about exhaustivelydefining all the ways that users of an SMW may become SMW related, butrather the concept that some users of an SMW will be SMW related to eachother, and others will not be SMW related. In an embodiment, the user ofcomputing device 104 will be referred to as a first user and other usersassociated to the first user through the SMW will be referred to asassociates of the first user.

Server computer 102 may be a laptop computer, tablet computer, netbookcomputer, personal computer (PC), a desktop computer, a personal digitalassistant (PDA), a smart phone, an SMS capable phone, or anyprogrammable electronic device capable of communicating with computingdevice 104 and SMW server 106 via network 110, in accordance with one ormore embodiments of the invention. Server computer 102 includesrecommendation program 112 having request module 113, review module 114,SMW module 116, and recommendation module 118. Recommendation program112 generates a targeted menu item recommendation in response to a userrecommendation request.

In an embodiment, request module 113 is a collection of machine readableinstructions and data that is used to receive and analyze informationcontained in the user recommendation request. Review module 114 is acollection of machine readable instructions and data that is used toretrieve, manage, and control information gathered from public sourcesor application based review platforms regarding the user recommendationrequest. SMW module 114 is a collection of machine readable instructionsand data that is used to retrieve, manage, and control information fromSMWs (e.g., SMW server 106) regarding the user's request and todetermine SMW associations. Recommendation module 118 is a collection ofmachine readable instructions and data that is used to retrieve, manage,and control information collected by review module 114 and SMW module116 to generate a targeted menu item recommendation.

A detailed block diagram of recommendation program 112 is provided inFIG. 2. An exemplary embodiment of steps performed by recommendationprogram 112 is provided in FIG. 3. Recommendation program 112 may run onserver 102 (as illustrated), computing device 104, and/or on anydevice(s) connected through network 110. Server 102 and computing device104 may include internal and external hardware components, as describedin reference to FIG. 4.

FIG. 2 is a functional block diagram of recommendation program 112,according to an embodiment. More specifically, the modules and processflow of recommendation program 112 are provided in further detail.

As illustrated, recommendation program 112 includes request module 113,review module 114, which further includes fetch module 115, SMW module116, which further includes fetch module 117, and recommendation module118, which further includes value submodule 119 a and response submodule119 b, in accordance with one or more embodiments of the invention.Recommendation program 112 generates a targeted menu item recommendationin response to a user request, based on user preferences, onlinerestaurant menu item reviews, and recommendations and opinions ofassociates of the user on SMWs.

In an embodiment, request module 113 receives a menu item recommendationrequest from computing device 104 originating from the first user.Request module 113 performs SBTA to determine the relevant criteria forcollecting information regarding the menu item recommendation request.For example, the first user might request a menu item recommendation fora seafood dish containing fewer than 700 calories at a restaurant withina specific zip code. In this example, request module 113 may determinethat the first user is requesting a targeted menu item recommendationthat includes three categories of interest (i.e., type of dish, caloriecount, and location) which may be assigned tags or annotators to definesearch classes. Request module 113 can send the search criteria toreview module 114 and SMW module 116 for the collection of relevantinformation regarding the determined search criteria.

Fetch submodule 115 retrieves information relating to the searchcriteria from available sources such as online restaurant menus andreview guides. The software and data that makes up restaurant websitesand food review applications would be examples of informational sourcesfor fetch submodule 115. Fetch submodule 117 retrieves informationrelating to the search criteria from SMW's, such as Twitter® orFacebook®. The software and data that makes up Facebook® would be anexample of an informational source for fetch submodule 117.

In an embodiment, fetch submodules 115 and 117 retrieve information,based on the search criteria, and may assign an initial value to eachpiece of information relating to the degree of which the information canbe interpreted as a positive recommendation of a menu item. For example,if fetch submodule 115 finds three reviews meeting the search criteriabut each have different rating scales (e.g., 4 out of 5 stars, twothumbs up, or an overall positive review determined by text analysis),each review can be given a normalized initial value within a definedrange. If the defined range is 0-10, for example, 4 stars may be given anormalized initial value of 8, two thumbs up could be given a normalizedinitial value of 10, and an overall positive review may be given anormalized initial value depending on the context of the information asdetermined by the text analysis (e.g., “the dish was above average” maybe given a 7 or 8 out of 10). Review module 114 and SMW module 116 cansend the collected information from fetch submodules 115 and 117 torecommendation module 118 for further analysis. Alternatively, reviewmodule 114 and SMW module 116 may not assign any value to the collectedinformation, such that values are applied to the collected informationduring analysis in recommendation module 118.

In an embodiment, value submodule 119 a receives the collectedinformation from review module 114 and SMW module 116 and applies aweighting value to each piece of information based on, for example, userpreferences (e.g., vegan, likes cheese, dislikes spicy food) andassociation values (e.g., information linked to SMW associates). Theweighing value can be a multiplier, an additive value, or a value usingany other value adjustment technique.

Regarding user preferences, value submodule 119 a can collect userpreferences from the SMW (e.g., a post or profile of the first user),user defined preferences (i.e., preferences entered directly intorecommendation program 112), or any other available data collectionmethod regarding the user's preferences. For example, the first user mayenter the fact that he is lactose intolerant directly intorecommendation program 112 via a user interface on computing device 104or server 102. Additionally, recommendation program 112 may retrieveinformation from the SMW that includes a post by the first userexpressing his dislike for spicy food. Alternatively, the userpreferences may be applied during the initial valuation of review module114 and SMW module 116.

Regarding association values, value submodule 119 a can determine anassociation value for information collected by fetch submodules 115 and117 linked to an associate user. For example, a second user may be anassociate of the first user. Generally, if a user is an associate of thefirst user, the first user can make a better determination of therelevance of the review over a review received from a user that isunknown to the first user. Therefore, a higher weighting value may begiven to the associate's review. Value submodule 119 a may reviewinformation regarding the second user to determine if the second userhas similar preferences as the first user using SBTA (even though thefirst and second users are associates, the first user may not know everypreference of the second user). For example, a lower weighted value maybe given to a menu item review provided by the second user, where thesecond user gives a menu item a perfect rating and states that the menuitem was “very spicy.” Value submodule 119 a may apply the associationvalues to the normalized initial values applied by review module 114 andSMW module 116. Alternatively, the association values may be appliedduring the initial valuation of review module 114 and SMW module 116.

The weighting values can be based on any valuation system such as, forexample, a yes/no valuation system or a scaled valuation system. Forexample, value submodule 119 a may use a yes/no valuation system forfoods containing lactose because the first user is lactose intolerant.Such that, a menu item containing lactose may be given a value of zero(i.e., a “no” determination) essentially removing the menu item from thetargeted recommendation. Alternatively, value submodule 119 a may use ascaled valuation system for foods recommended by associates with similarpreferences versus associates with different preferences. For example, athird user may share a preference of menu items that are not spicy(oppose to the second user who likes spicy food), such that a higherscaled value will be given to a positive review given by the third userand a lower scaled value will be given to a positive review given by thesecond user (regarding the collection category of spicy foods).

Response submodule 119 b can receive the collected information (with theinitial value and/or weighted value) from value submodule 119 a togenerate the targeted recommendation. For example, value submodule 119 awill send the collected data with the applied weighted values toresponse submodule 119 b. Response submodule 119 b can organize thecollected information in a fashion set by the user or any other method.For example, the collected data may be organized in descending orderbased on the weighted values. Once the collected information isorganized by response submodule 119 b, the organized targetedrecommendation may be sent to the user via computing device 104.

FIG. 3 is a flowchart depicting operational steps of recommendationprogram 112, according to an exemplary embodiment.

Request module 113 receives a menu item recommendation request from auser via a computing device (Step 302). Request module 113 can performtext analytics on the recommendation request to determine the searchcriteria to be performed (Step 304). For example, request module 113 maydetermine that the first user is requesting a menu item recommendationfor a seafood dish containing fewer than 700 calories at a restaurantwithin a specific zip code. The search criteria can include three searchcategories (e.g., type of food, calorie count, and location). Fetchsubmodules 115 and 117 can collect information relevant to the searchcriteria from available online sources and SMW's (Step 306). Forexample, fetch submodules 115 and 117 can collect information frompublically available restaurant menus, review guides, and SMW's usingtext analytics. Fetch submodules 115 and 117 can assign an initial valueto each piece of information (Step 308). Value submodule 119 a can applya weighting value to the collected data based on user preferences andassociation value (Step 310). Response submodule 119 b can then send anorganized targeted menu item recommendation to the user via computingdevice 104 (Step 312).

FIG. 4 is a block diagram of components of the server computer 102, inaccordance with an illustrative embodiment of the present invention. Itshould be noted, computing device 104 may also include the samecomponents described herein.

Server computer 102 may include one or more processors 402, one or morecomputer-readable RAMs 404, one or more computer-readable ROMs 406, oneor more computer readable storage media 408, device drivers 412,read/write drive or interface 414, network adapter or interface 416, allinterconnected over a communications fabric 418. Communications fabric418 may be implemented with any architecture designed for passing dataand/or control information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system.

One or more operating systems 410, and one or more application programs411, for example, recommendation program 112 described in reference toFIG. 1, are stored on one or more of the computer readable storage media408 for execution by one or more of the processors 402 via one or moreof the respective RAMs 404 (which typically include cache memory). Inthe illustrated embodiment, each of the computer readable storage media408 may be a magnetic disk storage device of an internal hard drive,CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk, asemiconductor storage device such as RAM, ROM, EPROM, flash memory orany other computer-readable tangible storage device that can store acomputer program and digital information.

Server computer 102 may also include a R/W drive or interface 414 toread from and write to one or more portable computer readable storagemedia 426. Application programs 411 on server computer 102 may be storedon one or more of the portable computer readable storage media 426, readvia the respective R/W drive or interface 414 and loaded into therespective computer readable storage media 408.

Server computer 102 may also include a network adapter or interface 416,such as a TCP/IP adapter card or wireless communication adapter (such asa 4G wireless communication adapter using OFDMA technology). Applicationprograms 411 on server computer 102 may be downloaded to the computingdevice from an external computer or external storage device via anetwork (for example, the Internet, a local area network or other widearea network or wireless network) and network adapter or interface 416.From the network adapter or interface 416, the programs may be loadedonto computer readable storage media 408. The network may comprisecopper wires, optical fibers, wireless transmission, routers, firewalls,switches, gateway computers and/or edge servers.

Server computer 102 may also include a display screen 420, a keyboard orkeypad 422, and a computer mouse or touchpad 424. Device drivers 412interface to display screen 420 for imaging, to keyboard or keypad 422,to computer mouse or touchpad 424, and/or to display screen 420 forpressure sensing of alphanumeric character entry and user selections.The device drivers 412, R/W drive or interface 414 and network adapteror interface 416 may comprise hardware and software (stored on computerreadable storage media 408 and/or ROM 406).

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, an illustration of a cloud computingenvironment 500 is depicted, according to an exemplary embodiment. Thecloud computing environment 500 can include one or more cloud computingnodes 510 with which local computing devices used by cloud consumers,such as, for example, personal digital assistant (PDA) or cellulartelephone 540A, desktop computer 540B, and/or laptop computer 540C maycommunicate. The nodes may be grouped (not shown) physically orvirtually, in one or more networks, such as Private, Community, Public,or Hybrid clouds as described hereinabove, or a combination thereof.This allows the cloud computing environment to offer infrastructure,platforms and/or software as services for which a cloud consumer doesnot need to maintain resources on a local computing device. It isunderstood that the types of computing devices 540A-C are intended to beillustrative only and that computing nodes and the cloud computingenvironment can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 500 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 600 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 601;RISC (Reduced Instruction Set Computer) architecture based servers 602;servers 603; blade servers 604; storage devices 605; and networks andnetworking components 606. In some embodiments, software componentsinclude network application server software 607 and database software608.

Virtualization layer 670 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers671; virtual storage 672; virtual networks 673, including virtualprivate networks; virtual applications and operating systems 674; andvirtual clients 675.

In one example, management layer 680 may provide the functions describedbelow. Resource provisioning 681 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 682provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 683 provides access to the cloud computing environment forconsumers and system administrators. Service level management 684provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 685 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 690 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 691; software development and lifecycle management 692;virtual classroom education delivery 693; data analytics processing 694;transaction processing 695; and targeted recommendation processing 696(e.g., recommendation program 112).

Based on the foregoing, a computer system, method, and computer programproduct have been disclosed. However, numerous modifications andsubstitutions can be made without deviating from the scope of thepresent invention. Therefore, the present invention has been disclosedby way of example and not limitation.

What is claimed is:
 1. A computer-implemented method for generating atargeted menu item recommendation, the method comprising: receiving, byone or more computer systems, a menu item recommendation requestoriginating from a user; generating, by one or more computer systems,search criteria based on the received menu item recommendation requestusing text analytics; retrieving, by one or more computer systems, menuitem reviews meeting the search criteria; simultaneously assigning, byone or more computer systems, a weighted value to each of the retrievedmenu item reviews using text analytics based on text of the menu itemreviews, preferences of the user, and association values, wherein theassociation values are based on retrieved menu item reviews linked tosocial media website (SMW) associates of the user; simultaneouslymodifying, by one or more computer systems, each of the weighted valuesof each retrieved menu item review based on the association values,wherein the retrieved menu item reviews linked to the SMW associates ofthe user are given more weight than the retrieved menu item reviews ofnon-associates of the user; simultaneously decreasing, by one or morecomputer systems, each of the weighted values of each retrieved menuitem review linked to the SMW associates of the user based onpreferences of the SMW associates of the user determined using textanalytics; generating, by one or more computer systems, the targetedmenu item recommendation based on the weighted values assigned to theretrieved menu item reviews; and sending the targeted menu itemrecommendation to a user's device.
 2. The computer-implemented method ofclaim 1, wherein the targeted menu item recommendation organizes themenu item reviews in descending order of weighted values.
 3. Thecomputer-implemented method of claim 1, wherein the search criteriaincludes annotators to define search classes.
 4. Thecomputer-implemented method of claim 1, wherein the available menu itemreviews include menu item reviews and menu item ingredients.
 5. Thecomputer-implemented method of claim 1, wherein the weighted valueslinked to SMW associates of the user are higher than weighted values notlinked to SMW associates of the user.
 6. The computer-implemented methodof claim 1, wherein the user preferences are defined by user informationdetermined using text analytics.
 7. A computer program product forgenerating a targeted menu item recommendation, the computer programproduct comprising: one or more computer-readable storage media andprogram instructions stored on the one or more computer-readable storagemedia, the program instructions comprising: program instructions toreceive a menu item recommendation request originating from a user;program instructions to generate search criteria based on the receivedmenu item recommendation request using text analytics; programinstructions to retrieve menu item reviews meeting the search criteria;program instructions to simultaneously assign a weighted value to eachof the retrieved menu item reviews using text analytics based on text ofthe menu item reviews, preferences of the user, and association values,wherein the association values are based on retrieved menu item reviewslinked to social media website (SMW) associates of the user; programinstructions to simultaneously modify, by one or more computer systems,each of the weighted values of each retrieved menu item review based onthe association values, wherein the retrieved menu item reviews linkedto the SMW associates of the user are given more weight than theretrieved menu item reviews of non-associates of the user; programinstructions to simultaneously decrease, by one or more computersystems, each of the weighted values of each retrieved menu item reviewlinked to the SMW associates of the user based on preferences of the SMWassociates of the user determined using text analytics; programinstructions to generate the targeted menu item recommendation based onthe weighted values assigned to the retrieved menu item reviews; andprogram instructions to send the targeted menu item recommendation to auser's device.
 8. The computer program product of claim 7, wherein thetargeted menu item recommendation organizes the menu item reviews indescending order of weighted values.
 9. The computer program product ofclaim 7, wherein the search criteria includes annotators to definesearch classes.
 10. The computer program product of claim 7, wherein theavailable menu item reviews include menu item reviews and menu itemingredients.
 11. The computer program product of claim 7, wherein theweighted values linked to SMW associates of the user are higher thanweighted values not linked to SMW associates of the user.
 12. Thecomputer program product of claim 7, wherein the user preferences aredefined by user information determined using text analytics.
 13. Acomputer system for generating a targeted menu item recommendation, thecomputer system comprising: one or more computer processors; one or morecomputer-readable storage media; and program instructions stored on thecomputer-readable storage media for execution by at least one of the oneor more computer processors, the program instructions comprising:program instructions to receive a menu item recommendation requestoriginating from a user; program instructions to generate searchcriteria based on the received menu item recommendation request usingtext analytics; program instructions to retrieve menu item reviewsmeeting the search criteria; program instructions to simultaneouslyassign a weighted value to each of the retrieved menu item reviews usingtext analytics based on text of the menu item reviews, preferences ofthe user, and association values, wherein the association values arebased on retrieved menu item reviews linked to social media website(SMW) associates of the user; program instructions to simultaneouslymodify each of the weighted values of each retrieved menu item reviewbased on the association values, wherein the retrieved menu item reviewslinked to the SMW associates of the user are given more weight than theretrieved menu item reviews of non-associates of the user; programinstructions to simultaneously decrease each of the weighted values ofeach retrieved menu item review linked to the SMW associates of the userbased on preferences of the SMW associates of the user determined usingtext analytics; program instructions to generate the targeted menu itemrecommendation based on the weighted values assigned to the retrievedmenu item reviews; and program instructions to send the targeted menuitem recommendation to a user's device.
 14. The computer system of claim13, wherein the targeted menu item recommendation organizes the menuitem reviews in descending order of weighted values.
 15. The computersystem of claim 13, wherein the available menu item reviews include menuitem reviews and menu item ingredients.
 16. The computer system of claim13, wherein the weighted values linked to SMW associates of the user arehigher than weighted values not linked to SMW associates of the user.17. The computer system of claim 13, wherein the user preferences aredefined by user information determined using text analytics.